Evaluate Machine Learning Models with Yellowbrick
-
Updated
Jun 28, 2020 - Jupyter Notebook
Evaluate Machine Learning Models with Yellowbrick
A real world data analysis and sentiment analysis using NLP and supervised classification machine learning model #4
A Data-Driven Approach to Predict the Success of Bank Telemarketing
Учебные проекты курса "Специалист по Data Science", Яндекс Практикум
Kaggle Competition Home Credit Default Risk
Increasing your Reddit karma. Help Reddit Moderators improve karma by autosubmitting posts to the correct subreddit. Reddiquette: Cross-post if it belongs to either or both subs?
This repository contains codes for running naive bayes and k-NN classification algorithms on large dataset in python
Sample project of fraud detection using Machine-Learning algorithms and Mathematical tools (roc)
R | Classification Project
Design of classification model to predict customer churn rate.
Data analysis, visualization and prediction for the prevention of heart disease
simple script for plotting precision recall curves
Predict fraudulent credit card transactions using TensorFlow, Keras, K Neighbors, Decision Tree, SVM Regression and Logistic Regression classifiers .
This is a fake news classification project, using TFIDF and pre-trained w2v embedding as separate sets of features, along with text sentiment scores, to classify news text as fake or real.
Fully connected neural network analyzing sentiments in reviews for Amazon's Alexa.
This analysis helps predict cardiovascular diseases in human body.
На основании данных о поведении клиентов построить модель с максимально большим значением F1 для задачи классификации, которая будет определять клиентов, склонных к оттоку.
Project building ML & DL models to detect spam messages.
Ciência de Dados
Add a description, image, and links to the roc-auc topic page so that developers can more easily learn about it.
To associate your repository with the roc-auc topic, visit your repo's landing page and select "manage topics."